v1.0.0 — Production Ready YOLO

Enterprise YOLO Orchestration

wyolo is the definitive framework for scaling YOLOv8 and RT-DETR training. Seamlessly integrated with MLflow, DVC, and S3, it transforms computer vision experiments into industrial-grade pipelines.

100%

Automated Pipelines

9.5+

Pylint Score

MLflow

Native Integration

GPU

Optimized Workers

Core Capabilities

Scalable Computer Vision

Designed for organizations that need to manage hundreds of models with total traceability.

Native MLflow

Automatic logging of metrics, hyperparameters, and artifacts. One-click model registration in the enterprise registry.

DVC Traceability

Track every byte of your training data. Full reproducibility guaranteed through native Data Version Control integration.

Auto-Batch Optimization

Intelligent GPU memory detection. Automatically scales batch sizes to maximize throughput without OOM errors.

State Machine Workers

Robust worker implementation that handles dataset checks, GPU verification, and training cycles with error recovery.

S3/MinIO Storage

Centralized artifact management. Sync your weights, plots, and results to secure enterprise storage automatically.

Headless CLI

Powerful command-line interface for CI/CD integration and hyperparameter tuning sweeps.

Developer First

Simple API,
Infinite Power

Initialize a trainer with your enterprise config and let wyolo handle the complexities of MLOps lifecycle.

  • Automatic experiment creation
  • Real-time GPU monitoring
  • Automated weight uploading
from wyolo.core import TrainerWrapper

# Standardized Config
config = {
    "model": "yolov8n.pt",
    "mlflow": {"uri": "http://mlflow.corp"}
}

# Power up the training
trainer = TrainerWrapper(config=config)
trainer.create_model("yolov8n.pt", "detect")
trainer.train(config_train=config["train"])
Wisrovi

Wisrovi Ecosystem

wyolo is part of the Wisrovi AI suite, designed to bridge the gap between academic research and industrial computer vision implementation.

Visit wisrovi.dev

Build your Vision with wyolo